Device system for the autonomous generation of useful information

ABSTRACT

An artificial neural network-based system and method for determining desired concepts and relationships within a predefined field of endeavor, including a neural network portion, which neural network portion includes an artificial neural network that has been previously trained in accordance with a set of given training exemplars, a monitor portion associated with the neural network portion to observe the data outputs produced by the previously trained artificial neural network, and a perturbation portion for perturbing the neural network portion to effect changes, subject to design constraints of the artificial neural network that remain unperturbed, in the outputs produced by the neural network portion, the perturbation portion operable such that production of an output by the neural network portion thereafter effects a perturbation of the neural network portion by the perturbation portion, the monitor portion responsive to detection of the data outputs being produced by the previously trained neural network, whereby the system is operable to derive over a period of time a plurality of input/perturbation/output mapping relationships that differ from the input/perturbation/mapping relationships of the training exemplars.

This application is a continuation of U.S. application Ser. No.08/910,290, filed Aug. 13, 1997, now U.S. Pat. No. 6,018,727 which was adivision of U.S. application Ser. No. 08/323,238, filed Oct. 13, 1994,now U.S. Pat. No. 5,659,666.

The present invention relates to a system and process for simulating theinternal imagery and additional mechanisms which together emulatecreativity in the human mind. The system allows for the totallyautonomous generation of new concepts, designs, music, processes,discovery, and problem solving using recent developments in the area ofartificial neural network (ANN) technology. Several examples of the typeof useful information that can be obtained using the present technologyare set forth and described herein. The present system can be used totailor machine responses thereby making computers less rigid incommunicating with and interpreting the way a human responds to variousstimuli. In a more generalized sense, the subject system supplies theequivalence of free-will and a continuous stream of consciousnessthrough which the system may formulate novel concepts or plans of actionor other useful information.

Prior to this invention, artificial neural network (ANN) emulations ofbiological systems were used for non-creative tasks such as patternrecognition, neural control, and the generalization of experimentaldata. The present system represents a new approach and a new applicationof ANN's in which the system synthesizes novel plans of action andoriginal designs or creations. These systems, which we refer to asautonomous systems or “creativity machines” may perform imaginativefeats that extend beyond technological invention into the realms ofaesthetics and emotions.

The present preferred embodiment of the system employs two essentialcomponents, namely, (1) a neural network containing training in someproblem domain, which neural network is subjected to perturbations and,as a result of the perturbations, continuously outputs a stream ofconcepts, and (2) a monitoring portion, such as, in one particularpreferred form, a second or patrolling neural network, which portionconstantly monitors the outputs of the first network for variousreasons, such as to identify and isolate useful outputs. This tandemarrangement may be thought of as constituting a model of creativity, andperhaps attentional consciousness, and this internal imagery isspontaneously generated within the perturbed network, while themonitoring portion is constantly alert to the occurrence of certainoutputs, such as specific images possessing either utility or otheruseful characteristics including aesthetic appeal. The perturbationsused may be achieved by any number of different means including by theintroduction of noise, relaxation or degradation of the network and soforth. The two components discussed above will be described in moredetail hereinafter.

It is important to emphasize that the present systems need notnecessarily accept external information. Instead, the system may beallowed to operate such that information emerges spontaneously as aresult of any number of stochastic and/or systematic processes appliedto the characterizing parameters of the networks involved. With thistandem arrangement of the free-running neural network and the associatedmonitoring or policing portion, it is possible to generate a notion thatis superior in quality to anything generated by a known system, deviceor machine similarly exposed or perturbed.

DISCUSSION OF THE PRIOR ART

The inventor has demonstrated that the application of certain types ofnoise to the inputs or weights of an ANN may produce novel outputs ifthe vector completion process fails to activate an output vectorencountered during the network's training. Such outputs generally takethe form of a combination of known training outputs and generallyemulate the environment in which it was trained. Therefore, a neuralnetwork trained to generate the surface profiles of some device orobject such as a known mountain range would tend to produce veryplausible but unfamiliar mountain ranges if the inputs are subjected torandom stimulations. Similarly, a neural network trained to only produceclassical music would tend to produce potential classical themes whenexposed to random inputs. The inventor has shown that static networkshave produced some very novel outputs which have been detected withinmathematical studies. In all known cases, however, they have beenisolated by a human operator for their novelty. In contrast, the presentsystem autonomously monitors the output of such a network and canoperate to identify correspondences with or differences frompredetermined criteria associated with the monitoring portion forvarious purposes, such as, in a preferred embodiment, to select emergentconcepts, which may include courses of action, and which arerepresentative of activation patterns of neurons, on the basis of somepredetermined criteria established within a policing or patrollingneural network which, in such embodiment, is the monitoring portion ofthe system. Such concepts may include producing music or musical themesfor some purpose, or for designing some device such as a coffee mug, orproducing a process planning operation, or solving a problem, such as toseek a target figure of merit in a target seeking application of thesystem, and for many other applications, some of which will be describedmore in detail hereinafter.

Known ANNs have obtained a relatively high degree of precision in someareas such as in input-output mapping. The present invention teaches theuse of deliberate degradation of an ANN and therefore a corruption ofsuch precise mapping to produce useful information. Thus a networktrained to duplicate some knowledge domain may generate fairlyrepresentative examples of known devices at low levels of networkdegradation. For example, in the case of automobile design the knownnetworks may generate fairly representative examples of existing cars atlow levels of network degradation owing to the constraints existingwithin the network. In other words sensible designs are produced. Atprogressively higher levels of network degradation, such networkconstraints further relax to produce novel and more unusual hybridautomobile designs, some of which may fill a useful application niche ormarket. The key to making the transition from the ordinary to the novelis achieved by the control over the network degradation and the abilityto relax or perturb certain network parameters from their trained-invalues. Thus the present system provides a way to design around theordinary or the near ordinary and to create new designs in much the samemanner as a creative designer would do, unlimited by certainconstraints. As a result of the introduction of various forms ofperturbations to the inputs, internal activations, weights and biases,such known systems may control a process or create an object or design.The information thus produced with the present system may be stored forlater use to control a process or the like and/or used in its ownautonomous decisions to modify the output or outputs that have beenproduced in some desired fashion. Thus the present system providesanother tool, and a very broad based tool, for doing design or creativework, including as part of target seeking applications, throughutilization of the two elements discussed above. It is contemplated,however, to fine-tune or toggle the subject system to autonomouslychange its mode of operation from performing one task to performing adifferent task or different purpose.

Being able to internally modify the network in a myriad of ways allowsfor vast numerical superiority in the number of viable concepts that maybe produced. The present tandem arrangement of system elements allowsfor complete autonomy in this task.

OBJECTS OF THE INVENTION

It is a principal object of the invention to teach the construction andoperation of novel means for simulating creativity.

Another object is to perturb artificial neural networks, previouslytrained, in order to produce useful and imaginative output information.

Another object is to monitor output information from a perturbed neuralnetwork in order to select desired outputs and reject others.

Another object is to produce controllable changes in a neural network bycontrolling the extent of perturbations applied thereto.

These and other objects and advantages of the present invention willbecome apparent after considering the following detailed specificationof preferred embodiments in conjunction with the accompanying drawingswherein:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system that depicts a system portionentitled imagination engine (IE) in association with another systemportion entitled alert associative center (AAC) connected to operateaccording to the teachings of the present invention;

FIG. 2 illustrates how perturbations from an external source are appliedto a particular embodiment of the present system to produce a pluralityof outputs any one or more of which can be selected to represent thedesired output information (mode A);

FIG. 3 illustrates how perturbations in the form of connection weightpruning can be applied to the system of FIG. 2 to produce a plurality ofoutputs any one or more of which can be selected to represent thedesired output information (mode B);

FIG. 4 illustrates how perturbations in the form of connection weightprunings can be applied to a recurrent network to produce a plurality ofoutputs which can be selected to represent a novel procedure (mode C);

FIG. 5 is a diagram of one embodiment of the subject system used indesigning and/or producing a desired shape for a coffee mug;

FIG. 6 is a block diagram of the means employed to produce the coffeemug of FIG. 5;

FIG. 7 is a view showing one form of the operating members controlled bythe subject system in the production of the desired shape for a coffeemug;

FIG. 8 shows examples of acceptable and unacceptable coffee mug designs;

FIG. 9 depicts an embodiment of the subject system, illustrating themanner in which the inputs and outputs of the subject system can be usedfor producing musical verses utilizing a recurrent network;

FIGS. 10A-10C illustrate network activations employed in the productionof acceptable music where the training produces a combination of thesongs “TWINKLE, TWINKLE LITTLE STAR”, “DAISY” and “MARY HAD A LITTLELAMB”;

FIG. 11 shows a manner in which the subject system can be employed toconvert the outputs of the IE of FIG. 10A to sounds;

FIG. 12 shows ten (10) musical phrases produced by the system embodimentdepicted in FIG. 9;

FIG. 13 depicts an embodiment of the subject system, illustrating amanner in which the inputs and outputs of the subject system can be usedfor producing musical phrases utilizing a simple feed forward networkand perturbations applied to both inputs and connection weights of thatnetwork;

FIGS. 14A and 14B show musical phrases of acceptable form produced usinga non-recurrent feed forward network;

FIGS. 15a- 15 b show fifty (50) novel musical themes or phrases producedby the system of FIG. 13;

FIG. 16 depicts an embodiment of the subject system, illustrating amanner in which the inputs and outputs produced by such embodiment canbe used for producing novel automobile designs; and

FIGS. 17A and 17B show two automobile designs produced by the system ofFIG. 16, including a design (1) to achieve at least 35 PMG, cost lessthan $25,000.00 and have a favorable rating in terms of usersatisfaction and design (2) which is an automobile capable ofaccelerating to 60 MPH in less than eight (8) seconds and achieve a topspeed of at least 150 MPH.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to the drawings more particularly by reference numbers, number20 in FIG. 1 refers to a preferred system constructed according to thepresent invention. The system 20 includes two basic components, onelabeled imagination engine (IE) 22, which is an artificial neuralnetwork (ANN) that is subjected to perturbations, sometimes in aprogressive manner, as will be discussed further hereinafter, whileproducing outputs which it feeds to a second, monitoring component,comprising what is hereafter generally referred to as an alertassociative center (AAC) 24, which component is a system portion thatmonitors the IE 22 and which, in some preferred embodiments, may also bean artificial neural network, and which, in turn, may have one or morefeed back connections 28 to the IE 22. The IE or imagination engineconstitutes that portion of the subject system that receives the inputinformation in the form usually of stochastic noise or perturbationsapplied against the training of the IE and is applied to its weights,biases, inputs, outputs or internal activations. The imagination engineis so described in order to convey the idea that this network isperturbed either internally or externally, and as a result of attemptingto perform pattern completion in the presence of the perturbations ornoise, produces outputs which freely wander through some given knowledgedomain which is embodied in the network's training. The outputs can alsobe recycled. The outputs of the IE are monitored or patrolled by theAAC. The IEs and the AACs may be grouped or coupled into one or more ofa plurality of relationships depending upon what is to be accomplishedand the nature of the inputs and outputs that are required. The IE andAAC can be combined into more complex systems involving a plurality ofcoupled ANNs and is able to perform more involved tasks includingproblems that require a certain degree of creativity, as will be furtheraddressed hereinafter with regard to FIG. 16.

It has been discovered that it is common to all neural networks thatwhenever a neural network is subjected to a synaptic perturbationprocess wherein connection strengths between neurons are progressivelyaltered, such a process activates the memories of the environmentalfeatures the network has already seen or been trained in. Thereafter, asthe network continues to evolve from its initial trained state, suchenvironmental features are combined into hybridized or juxtaposedfeatures. For example, a neural network trained to produced the imagesof various animals, including those of a cow and a bird, would firstproduce random, intact images of these animals and birds during lowlevels of synaptic perturbation. Progressively stronger perturbations insynaptic connection strength would now produce hybridized images,including that of a flying cow, something that is part cow, part birdand so forth. In other words, the same universe embodied within the IEhas begun to unravel as constraints are gradually removed and new,unheard of combinations emerge. By the same token, intact neuralnetworks, either artificial or biological, may be activated to producenovel environmental images by noise or other processes occurringexternally to themselves. Such noise may be generated by the function,relaxation or degradation of other surrounding networks andcommunicating biological networks or complex network implementations.The results will be similar to that when using internally generatedperturbations, with a spontaneous appearance of both straight forward aswell as hybridized environmental features.

This can also be accomplished by constructing the IE of a plurality ofneurons so that some portion of the processing units remains unrecruitedto the required training or mapping. Application of any of theperturbing influences to these uncommitted neurons can producesignificantly increased generation of useful concepts, which can in turnbe captured or processed or selected by the AAC. The AAC, which is thesecond major component of the subject system as depicted in FIG. 1,forms all or part of a monitor portion that operates in some of thepreferred embodiments, as will be discussed in greater detailhereinafter, to monitor and identify useful information orjuxtapositions produced by the IE. The monitor portion or AAC istherefore an opportunistic component that is on the lookout for certainfeatures or conditions, such as correspondences with or deviations fromestablished criteria, which, in our present example, would entaillooking out for particular animals or the like. In a typical situation,the AAC can be designed or trained to assign numerical or other scoresto the hybrids or results synthesized by the IE. One or more separatealgorithms can form or be associated with the AAC for such purposes andto store potentially useful concepts for later consideration andrefinement, or alternatively can be used to immediately influenceresults in a hardware condition. In many of the more detailedembodiments depicted and discussed herein, the AAC is, like the IE, alsoselected to be an ANN, which has been trained to identify usefulinformation or juxtapositions produced by the IE and which can also betrained to assign numerical values to the hybrids synthesized by the IE.In some embodiments it is also contemplated that some of the inputs tothe AAC may not be connected to outputs of the IE but may be left freefor other purposes. In this way the AAC selection criteria can beadjusted initially or during operation for example as shown in FIG. 3.

Three different modes of operation for the combined IE and AAC neuralnetwork object (which combination can encompass various degrees orlevels of distinction and/or integration of the IE and AAC when both areneural network objects) will be discussed hereinafter. These modes canbe used separately or in various combinations. These are described asmodes A, B and C. In mode A, any number of techniques, including randomnumber generation, may be used to supply novel inputs to the IE. Thisresults in the IE attempting vector completion on the novel inputs,usually resulting in some change or juxtaposition of its establishedtraining outputs. The AAC then checks the utility of these resultinghybridized outputs from the IE and assigns values to select criteriashown as A-Z in FIG. 2. When the selection criteria are met, thehybridized output may then be immediately utilized or recorded for lateruse.

In mode B, fixed values are clamped to the inputs of the IE while itsweights, biases, or internal activations are perturbed by any number ofnumerical techniques, including random number generation to perturb themfrom their original values. An internal completion process within thenetwork layers produces new conditions or juxtapositional concepts whichemerge at the outputs of the IE. The AAC then rates these in terms oftheir usefulness based on its own training. As in mode A, these newconcepts may be utilized in real time as the network relaxes or they maybe saved in an archival file for later use.

In mode C, the IE perturbation is used to create a plan of action or aprocedure to solve a given problem. An example of such a system is shownin FIG. 4. In this example the IE feeds its own outputs back to itsrespective inputs and the procedure consists of a sequence of steps,each of which is contingent upon the prior step. In mode C, the AACexamines each step to assure its utility in forming an allowable anduseful step in arriving at the desired result. Also in mode C, the AACmay be used to modify the architecture of the IE at any stage, forinstance, by the removal, modification, or replacement of any givenweight or step. In mode C, an algorithm governing the operation couldhave weights randomly chosen within the IE and set to constant values,for example zero. The AAC would then evaluate the system configurationfor its utility or other purpose. If the evaluated state is not anallowable one, the AAC would make the decision to replace thetemporarily removed weight and inform the driving algorithm to select anew weight for removal. Once the desired system configuration isobtained, the system begins to remove more weights from the IE. The AACis alert to whether the overall target configuration was obtained. If itwas, the algorithm stores the successful sequence of operation whichconstitutes a procedure or it would immediately convey this informationto control some external device or hardware mechanism. In this way anANN serving as the IE assists, in a manner somewhat similar to the waythe human brain works, storing a concept or idea using a computer or thelike. This can be done in the present system by having a humanparticipant or the machine user produce feed backs to the IE in whichdifferent perturbations are applied to the IE network for some purposesuch as to boost or change its outputs. By using multiple IEs and AACsmore complex outputs can be obtained and in some cases more accurate andprecise data can be produced. For example, many separate networks ofthis type can be used in the concept selection process therebynecessitating the use of many different AACs.

In any of the above modes or juxtapositions any combination ofperturbing factors can be used to generate novel concepts within the IE.Such perturbations may combine different factors such as (a) weights,(b) biases, (c) activations, (d) external input signals, (e) internalinput signals to any given unit within the network, or (f) internaloutput signals from any given unit within the network. In like mannerthe parameters a-f may be perturbed by various means such as by (1)successively setting their values to some constant value such as zero;(2) successively adding some random number as obtained with a randomnumber table to their original values; (3) successively changing theirvalues by replacing them with random numbers obtained from a randomnumber table; (4) multiplying their values by a time-dependent factorusually with some decay constant; (5) successively adding positive andnegative random numbers obtained through a random number table to allowthese parameters to perform a random walk about the original values; (6)adding numbers which obey certain statistical frequency distributions ofthe form where the probability of choosing such a number obeys aprobability function; (7) adding numbers which obey set time-dependentstatistical distributions; and/or (8) progressively multiplying any ofthe above factors by some gradually increasing amplitude factor so as tosmoothly transition the IE from its original constrained condition,implicit within its training, to progressively relax constraints as theweights and biases stray from their training values. It has been foundthat enhanced results may be obtained by specifically applying suchperturbating influences to neurons which have not been fully recruitedinto the network's mapping or training.

In partial summary, an autonomous search procedure to arrive at novelconcepts has been described, and such a search procedure is applicableto different knowledge domains. The novel outputs or problem solutionsare arrived at through the interaction of two components: a first ANN,oftentimes referred to herein as an IE, and an associated monitorportion, oftentimes referred to herein as, or including, an AAC, whichmonitors the outputs of the first ANN and which can also itself take theform of or include an ANN in various particular embodiments, as will bediscussed in greater detail hereinafter. The first neural network istrained to produce outputs within the knowledge domain of its training,as a consequence of which input-output mapping is produced that models aparticular problem. One can then seek a given pattern in the output fromsuch neural network by applying perturbations to such neural networkuntil the desired pattern in the output is produced. The introduction ofsuch perturbations to any number of ANN features causes the first neuralnetwork to wander through the knowledge domain producing meaningfuloutputs under the constraints of its connection strengths and biases. Asthe level of the network perturbations increases, the constraints beginto more dramatically relax from their trained-in values andunconventional conceptual juxtapositions emerge which can be detectedand utilized by the monitor portion to alert an associative center oroutput device, such as when a targeted figure of merit is obtained or adesired output pattern is realized. The major strength of this techniqueis its ability to gradually and systematically perturb the IE networkfrom a state in which it simply duplicates known features of itsknowledge within its knowledge domain to a subsequent state ofperturbation in which ever so slightly new juxtapositional or hybridizedconcepts emerge. The subtle changes from the conventional to the mildlyunconventional produce new and potentially useful inventions which canbe autonomously identified and selected by the monitor portion. Thus thepresent system limits its search space in seeking solutions to manydifferent problems and it does so in a unique manner.

Coffee Mug Design

Referring now to FIGS. 5-8 there is shown a particular embodiment of thesubject system that can be used in the design and production of devicessuch as a novel coffee mug and the like. The subject coffee mug can beaesthetically pleasing and also serve a useful function. It is apparent,however, that the subject system is not limited to the production ofcoffee mugs and can be used to produce many other designs and shapesincluding works of art. It is also possible to interface the subjectsystem with a lathe, stereo lithographic, or other operating deviceincluding a potter's wheel to cause the subject system to produce in athree dimensional form, the object in question, in this case a coffeemug. In FIG. 5 the subject system embodiment 20 is illustrated on theright hand side and a potter's wheel 47 with a mound of clay 46 mountedon it is shown on the left side. Also shown in FIG. 5, in illustrativeform, is a device or operator member 48 which operates against the lumpof clay 46 to form the coffee mug into the desired shaped. The controlsfor the shaping means 48 are produced by the output of the IE 22 asselected by the beauty and function outputs which signal the controllingalgorithm to apply the IE outputs.

FIG. 6 is a logic flow diagram of the means shown in FIG. 5. The diagramincludes the IE 22, the AAC 24, which, in this embodiment, is also anANN, an output decision block 52 which has a NO output flowing back tothe IE on 58 and a YES output 54 labeled Implement Design which is ledback at 56 to the IE. The block 52 is labeled Does IE Output ExceedThresholds? The YES output 54 controls devices such as a template ormovable pins or the like in such a manner as to form the shape of themug.

FIG. 7 illustrates how the outputs of the various portions of the IE 22are used to control, in this case, solenoids 48 which have means thatengage the body of clay 46 to produce the desired shape. The solenoids62 are parts of the assembly 48 and are shown operated by spring tensionproduced by springs 60 and offset by the magnetic force generated by theelectrical currents proportional to the IE outputs.

FIG. 8 shows various coffee mug designs, including some which areaesthetically pleasing and utilitarian, and others which have minimalutility values. The cup designs are labeled 46A-46L.

In designing a coffee mug, various options should be assembled as to theaesthetic and utilitarian preferences and this information should beencoded in the AAC. This can be done using a computer code whichgenerates vertically aligned stripes of various lengths which togetherstimulate the profile or potential mug design. These designs can bedisplayed on a cathode ray tube or the like using stripes of randomlengths and widths and opinions can be gathered as to beauty and utilityusing ranking scores. The information thus obtained can be put on aspread sheet to be used for training the two separate neural networks,the IE and the AAC of this particular embodiment. The IE is trainedusing beauty and utility as the inputs and the AAC reverses thesignificance of the inputs and outputs so that the shape now serves asinputs and beauty/utility ratings and these become the outputs.

Music

FIGS. 9-14 and 15 a- 15 b illustrate another embodiment 70 of thesubject system being used to produce musical compositions or musicalphrases. Referring to FIG. 9, the embodiment 70 includes an IE 72 and anAAC 74 shown coupled together as in the previous constructions. The AACis trained to select from among the various outputs of the IE and toproduce an output musical rating at block 76, which rating is applied toa block 78 which is labeled Rating Exceeds Threshold? If the rating doesnot exceed the threshold then an output will be produced on lead 80which is applied to a block labeled Disregard New Note 82, and thisoutput is applied to other blocks including block 84 labeled RefreshWeights and block 86 labeled Perform Random Weight Prunings and fed backto the IE on lead 90. If the output of the block 78 is YES then outputswill be produced through the block 88 labeled Add New Note To The Bufferfor applying back to the IE to further modify the condition of the IE.The difference between FIG. 9 and the design previously discussed inconnection with the production of a coffee mug is that in FIG. 9 theoutputs are musical tones or the like arranged in a particular mannerdepending upon the training of the IE and the AAC, and the outputs aremusical phrases or themes as will be explained. In the production ofmusical compositions or themes the present embodiment employs a similarrelaxation technique that embodies modes A, B and C, as discussed above,and in so doing achieves a meaningful synthesis of the original musicalthemes. The IE in this embodiment is a recurrent network which learns toperform a number of songs such as “TWINKLE, TWINKLE LITTLE STAR”,“DAISY” and “MARY HAD A LITTLE LAMB”. The network as shown utilizes an8-10-8 architecture as shown in FIG. 9, with the outputs of the networkfed back as inputs. The first two inputs encode song (S1), (S2), thenext four (N1-N4) signify note order and the last two (FI, DI) containthe pitch and duration of any given note. The outputs of the networktake on similar significances with all the values taking on appropriatevalues for the next note within the musical sequence. The network asshown in FIG. 10A has four layers (1-4), denoted (L1-L4). The outputs ofthe network attain the same significance but now represent the next notein the sequence. There are two hidden layers of neurons each necessaryto achieve the desired mapping. This is shown in FIG. 10A by the twomiddle rows of neurons. By setting the left most inputs S1-S2 to valuesof (0,1), (1,0) or (1,1), the recurrent network would play “TWINKLE,TWINKLE LITTLE STAR”, “DAISY” and “MARY”, respectively. The applicationof random numbers to all of the inputs of the networks and in particularto S1 and S2 would cause the network to jump from one song to anothersong thereby juxtaposing tunes and producing music.

Referring again to FIG. 10 there is shown (1) network activation in theform of individual neurons shrinking and expanding according todifferent levels of activation, (2) a strip chart recording the mostrecent train of notes including their pitches, and (3) the output of aseparate network which has been trained to classify the output of theconcurrent network as a linear combination of any of the three trainingmelodies. This latter feature is helpful in envisioning the weighting ofindividual tunes within the hybridized songs. Such musical hybridizationis occurring in FIG. 10 where we see a combination of “TWINKLE” and“MARY” having been selected by the AAC and being performed. Training ofthe IE is accomplished by creating the map between successive noteswithin any particular song. As inputs the circuit is provided withbinary coded labels for the song number. For example, binary coded songnumbers 1-3 are input to nodes S1 and S2, binary coded note order 1-31are input to nodes N1-N4 and frequency and duration values are input tonodes F1 and D1. The targets for training include identical binary codedsong number (output notes S1 and S2), binary coded note orderincremented by a value of 1 (output nodes N1-N4) and the nextfrequency-duration pair of the song in output nodes F1 and D1.Wrap-around of the songs is achieved by mapping the last note in themusical sequence to the first. What has just been described refers tothe operation of the IE. It is now necessary to describe the functionand training of the AAC which operates on the output from the IE. Fortraining purposes a training computer code is written to generate aseries of 4 notes, consisting of a series of notes which obey a 1/fdistribution, a feature which is characteristic of all sounds we acceptas music. This series of tones is subjected to an evaluation after whichhuman evaluators were asked to respond on a 0-10 point scale to itsaesthetic value. After about 100 trials of this sort, the series offrequencies comprising each melody was passed to a spread sheet alongwith their consensus rankings. Inputs therefore consisted of the notesequences along with target values for training consisting of thenumerical scores. Real melodies were implanted within the training setand ranked at values of 10 for their appeal. Noise on the other handfrom a random generator was embedded within the spread sheet withranking values of 0. Following training on this data, the trained neuralnetwork IE and AAC were embedded within the same computer code.

The IE was placed within a loop wherein values between zero and 1 werefed to its inputs from a random number generator thus producing orgenerating composite melodies at the IE's outputs. A buffer containingat most four notes (4 frequency-duration pairs) played at anytime wassampled by the eight inputs of the AAC, rating each according to itsmusical acceptability. The best musical themes, those exceeding athreshold of 8 on the 10 point scale were played and archived to aseparate file. Generation of music composed by the subject machine wasachieved by the scheme shown in FIG. 11. Outputs from the IE deemedacceptable by the AAC are serially transmitted as a series of soundcommands involving both frequency and duration of successive notes tothe computer. These total commands are translated from digital to analogsignals and fed to a loud speaker where the sounds can be heard.Therefore as the subject machine generates acceptable note sequences, itinstantaneously relays them to an audio output device so that theoperator of the machine can hear the music that has been produced. Thenext 4 notes generated by the IE were similarly added to the buffer andplayed, and so on. Ten samples of these melodies captured by the AAC aredisplayed in FIG. 12. Their frequencies F are shown in Hz (1 octavebeing shown), while the duration D of each note is given in units of{fraction (1/18)} of a second. The combination of frequency and durationproduce sound.

A second approach to synthesizing original music involves using thesubject embodiment modified to consist of an IE of 3-16-20 nodalarchitecture and an AAC of 20-10-1 nodal architecture. The formerproduced a note sequence consisting of musical phrases or themes.Subsequently, the AAC checks this trial melodic phrase for its musicalacceptability. This is shown in FIG. 13. In this case the IE is trainedto perform pattern completion on the tonal frequencies (i.e., in Hz) ofthe first three notes within a given musical phrase. The musical phrasesfor training consist of very recognizable segments from twenty wellknown songs. Thus given the first three notes, the network was trainedto supply the remainder of that musical sequence. The output consistedof ten successive frequency-duration pairs with the frequency given inHertz and the duration in units of {fraction (1/18)} second.

In the same embodiment the AAC is trained by exposure to twenty ten notesegments from popular melodic themes, 50 examples of tones generatedfrom frequencies selected from a random number table and about 100 trialmelodies generated by the introduction of noise into a similar IEtrained only on noise and the above popular melodic themes. Targetvalues for training consisted of numerical scores of 1 for the popularthemes, numerical rankings of 0-1 on the IE generated melodies (asdetermined by human panelists,) and numerical scores of 0 for noisegenerated using a random number table.

Using various combinations of IE prunings, introduction of bothtime-varying inputs and time-varying perturbations to the internalconnection weights of the IE enabled the subject creativity machine torun autonomously until 11,000 musically acceptable themes or phrases hadbeen collected. Typical musical phrases created by this process areshown in FIG. 14. In FIGS. 15a- 15 b fifty representative musicalphrases produced by the subject embodiment are shown. At the top of thelisting are identifiers as to the frequency and duration for the numbersshown in each row. In other words, FIGS. 15a- 15 b show a number ofmusical themes audibly reproducible wherein the frequency and durationof each tone is indicated.

Automobile Design

Another application of an embodiment of the subject system demonstratesmodes A and B wherein the AAC is allowed to make autonomous decisions tomodify the architecture of the IE as well as to hold certain inputs tothe IE constant. The intent of this example is to design an automobileor car in terms of its major specifications and characteristics. This isdone based on some notions as to its desired performance, insurancecoverage, warranties, probability of theft, and anticipated usersatisfaction. In creating the subject embodiment, 29 performancecharacteristics of the design specification are shown as possible inputsto the IE in FIG. 16. The AAC, on the other hand, which, in thisembodiment is also an ANN, reverse maps from the design specificationsto performance characteristic. Both networks IE and AAC utilize a29-10-29 nodal architecture. The structure 100 shown in FIG. 16 includesan IE 102, an AAC 104, and an output 106 from the AAC which flows into adecision block 108 that bears the legend Does Candidate Auto Design MeetPerformance Criteria? The block 108 has a YES output 110 which flows toan archive design block 112 which in turn flows back to the input of thedevice 100 through branch 116. The output of the block 108 also flowsback to the input of the device 100 via branches 118 and 116. The branch116 flows to the input of the IE 102 by a block 120 labeled ApplyPerturbations to IE connection weight which provides the perturbationsto IE 102. The block 120 also flows to another block 122 labeled “SearchContinued For More Than N Cycles?” which has an output 124 that flows toblock 126 labeled Skeletonize IE which are applied as inputs to the IE102 and also has an output which flows to the input block labeled InputRandom Performance Factors to IE. The block 124 has another output whichflows through branch 130 to the same Input Random Performance Factorblock to inputs of the IE 102.

It will be appreciated that, in this particular embodiment, since theAAC is also an ANN, and since the block 108 operates to examine theoutput of the AAC and to determine whether the output performance datatherefrom satisfies desired performance criteria, block 108 functions asa monitor portion for a system that includes the ANN of AAC 104, whichmay be thus be considered the IE of such system, and AAC 104 can also beviewed as part of the monitor portion for another system that includesthe ANN of IE 102. Such particular embodiment is thus illustrative ofone manner in which a more complex system utilizing a plurality of thesubject ANN systems can be realized.

In operation, the IE inputs are fed values from a random number table.As each of these 29 component vectors are propagated through thenetwork, physically realizable automobile designs appear at its outputs,prescribed in terms of its 29 design specifications. Each set of outputsfrom the IE is then fed to the AAC 104 inputs wherein feed forwardpropagation produces the performance characteristics which would resultfrom that particular set of specifications. These performancecharacteristics are then compared with those desired to determinewhether or not the appropriate design has been obtained. This is what istrained into the AAC. If the design characteristics meet the criteria,the car design is displayed on some means such as a cathode ray tube. Ifthe design does not meet these requirements, additional random inputsare fed to the IE 102 and new pertubuations are applied to the IEconnection weighhts to generate another candidate design to be evaluatedin like fashion by the AAC 104. FIG. 17 shows the design of a carpossessing a highway mileage of at least 30 MPG, a retail price of atmost $25,000 and a projected user satisfaction of one on a (1), 0, (−1)rating scale that has been synthesized by the subject embodiment.

It is important to note that the network in this case is producing onlyphysically realizable car designs by carrying out vector completion onthe input vector consisting of random components as well as internalpertubations applied to the IE. Also, in this example, properties suchas curb weights and tire size are realistically scaled with factors suchas horsepower. Thus myriad nonsensical specifications are eliminatedwithin the IE via the soft constraints offered by the connectionstrengths within the trained neural network. If the subject embodimentis given numerous performance criteria search time will be extended.Therefore, if after a predetermined number of forward propagations suchas ten propagations through the IE 102, the performance criteria havenot been met, the algorithm would direct the controlled pruning ofweights from the IE 102 in an attempt to narrow the search down. In thiscase, weights are removed one by one from the IE, while propagating anumber of randomized input vectors through the entire device 100 to seeif the AAC output falls within a predetermined envelope of performancevalues that blanket the desired range. Should a weight be removed whichgenerates AAC outputs outside this range, it is replaced. Should a moreradical automobile design be required, systematic increase of theamplitude of the perturbations applied to the connection weights of theIE would be made, depending upon the AAC to predict the performance ofsuch hypothetical automobiles.

It will be obvious to those skilled in the art that the presentinvention can be used for many purposes other than the limited purposesdescribed herein. The important thing to recognize is that by using anIE and an associated monitor portion (AAC), especially when the IE andthe AAC are comprised of two neural networks or groups of neuralnetworks, one of which operates as the IE and the other as the AAC, itis possible to envision myriad possible uses therefor. These uses can bein designing, problem solving, selecting, developing manufacturingprocesses and many other areas.

Thus there has been shown and described a novel system which simulatescreativity and autonomously generates useful information for somepurpose. Many changes, modifications, variations and other uses inapplications for the subject system will suggest themselves, to thosefamiliar with the art. All such changes, modifications, variations andother uses in applications which do not depart from the spirit and scopeof the invention are deemed to be covered by the invention which islimited only by the claims which follow.

What is claimed is:
 1. An artificial neural network-based system fordetermining, for a specified knowledge domain in a given field ofendeavor as represented in a neural network, desired concepts andrelationships within such predefined field of endeavor, comprising aneural network portion having an output portion at which data outputsare produced, said neural network portion including an artificial neuralnetwork that has an input portion and which is operable to effectproduction of a data output from said output portion of said neuralnetwork portion when an input pattern is supplied to said artificialneural network at the input portion thereof, said artificial neuralnetwork having been previously trained in accordance with trainingexemplars in a given predefined field of endeavor to establish aparticular knowledge domain therein and being normally operable inaccordance with the constraints embodied in its design and theestablished knowledge domain to produce standard data outputs inresponse to input patterns supplied to said previously trainedartificial neural network at the input portion thereof, a monitorportion associated with said neural network portion to observe dataoutputs produced at the output portion of neural network portion, and anetwork perturbation portion for perturbing said neural network portionto effect changes, subject to constraints embodied in the design of thepreviously trained artificial neural network that remain unperturbed, inthe data outputs produced by said neural network portion at the outputportion of said neural network portion, said network perturbationportion operable such that production of a data output by said neuralnetwork portion thereafter effects a perturbation by said networkperturbation portion of said neural network portion, such perturbationdriving an operation of said artificial neural network to effectproduction of a data output from said neural network portion, the dataoutput so produced establishing, based in part upon the particularvaried perturbation effected, an input-perturbation-output mappingrelationship within said predefined field of endeavor, said monitorportion operable to detect and to identify, from among the data outputsbeing produced over a period of time at the output portion of saidneural network portion when said neural network portion is so perturbed,data outputs which satisfy certain predefined criteria as preselected bya user, identification of a data output that satisfies the predefinedcriteria determining a desired concept within the predefined field ofendeavor, which desired concept is associated with a particularinput-perturbation-output mapping relationship established duringoperation of said system.
 2. The system of claim 1 wherein the monitorportion includes a comparator portion that operates to identify fromamong the observed data outputs being produced at the output portion ofthe neural network portion certain data patterns in said observed dataoutputs which satisfy said predefined criteria.
 3. The system of claim 2wherein the network perturbation portion includes means for introducinginternal perturbations to said previously trained artificial neuralnetwork to thereby effect a change in the operation thereof, subject tothe constraints embodied in the design that remain in effect, to therebyeffect production at the output portion of said neural network portion,for given input patterns being supplied to said previously trainedartificial neural network, of data outputs that are distinct from thecorresponding standard data outputs that would be produced in responseto such given pattern of inputs by said previously trained artificialneural network in the absence of such network perturbation, whichdistinct data outputs remain subject to the unchanged constraintsembodied in the design of said previously trained artificial neuralnetwork but identify distinct concepts in the predefined field ofendeavor.
 4. The system of claim 3 wherein the predefined criteria havebeen selected such that a data output from said neural network portionwhich satisfies such predefined criteria is a desired concept within thegiven field of endeavor.
 5. The system of claim 3 wherein the monitorportion includes a program routine.
 6. The system of claim 3 wherein themonitor portion comprises a second previously trained neural network. 7.The system of claim 3 wherein a subsequent perturbation of said neuralnetwork portion is determined at least in part by the response of saidmonitor portion to a prior data output of said neural network portion.8. The system of claim 2 wherein the network perturbation portionincludes means for introducing external perturbations to said previouslytrained artificial neural network to vary the particular pattern ofinputs as presented at the input portion of said previously trainedartificial neural network to thereby establish, upon the production of adata output in response to the particular pattern of inputs aspresented, an input-output data pairing relationship.
 9. The system ofclaim 8 wherein the predefined criteria have been selected to representa particular desired pattern within a data output from said neuralnetwork portion, whereby, upon satisfaction of said predefined criteria,the determined input-output pairing relationship identifies a data inputthat results in the data output that satisfies such predefined criteria.10. The system of claim 8 further characterized in that the monitorportion includes a program routine.
 11. The system of claim 8 whereinthe monitor portion comprises a second previously trained neuralnetwork.
 12. The system of claim 8 wherein said means for introducingexternal perturbations includes a second previously trained artificialneural network and associated means for generating varied input patternsto said previously trained artificial neural network.
 13. The system ofclaim 1 wherein said network perturbation portion is operable to effectperturbations of said neural network portion in a substantially randommanner.
 14. The system of claim 1 wherein said artificial neural networkincludes an input layer, an output layer, and at least one hidden layer.15. The system of claim 1 including a storage medium associated withsaid monitor portion for retaining and storing data representative ofsaid established input-perturbation-output mapping relationships. 16.The system of claim 1 including a system output portion associated withsaid monitor portion for receiving data representative of saidestablished input-perturbation-output mapping relationships andformatting said data for use in external systems.
 17. A method fordetermining, for a specified knowledge domain in a given field ofendeavor as represented in a neural network, desired concepts andrelationships within such predefined field of endeavor, comprising thesteps of: (a) providing a neural network object having an output portionat which data outputs are produced and including an artificial neuralnetwork having an input portion and which is operable to effectproduction of data outputs at said output portion of said neural networkobject in response to input patterns supplied to said input portion ofsaid artificial neural network, the data outputs so produced havingparticular data patterns associated therewith, said artificial neuralnetwork having been previously trained in accordance with a set of giventraining exemplars in a predefined field of endeavor and having aparticular established knowledge domain for the predefined field ofendeavor, which previously trained neural network is normally operablein accordance with the constraints embodied in its design and itsestablished knowledge domain to produce at said output portion, inresponse to input patterns supplied to the neural network at the inputportion thereof, standard data outputs representative of known conceptsand having associated input-perturbation-output mapping relationships,(b) perturbing the neural network object to drive an operation of theneural network object and to effect a change in the data outputsproduced thereby, such changed data outputs havinginput-perturbation-output mapping relationships that differ from theinput-perturbation-output mapping relationships of the given trainingexemplars and their associated standard data outputs, (c) monitoring theneural network object over a period of time to detect the data outputsproduced thereby in response to perturbations and to identify from amongthe data outputs those data patterns which satisfy predefined criteriaof acceptability, identification of a data pattern that satisfies thepredefined criteria determining a particular concept within thepredefined field of endeavor, which concept has associated therewith aparticular input-perturbation-output mapping relationship.
 18. Themethod of claim 17 wherein step (b) includes the step of: internallyperturbing said artificial neural network to thereby effect a change inthe operation thereof, subject to the constraints embodied in the designthat remain in effect, to thereby effect production at said outputportion, for given input patterns being supplied to said previouslytrained artificial neural network, of data outputs that are distinctfrom the corresponding standard data outputs that would be produced inresponse to such given input patterns by said previously trainedartificial neural network in the absence of such network perturbation,which distinct data outputs remain subject to the unchanged constraintsembodied in the design of said previously trained artificial neuralnetwork but identify distinct concepts in the predefined field ofendeavor.
 19. The method of claim 17 wherein step (b) includes the stepof: introducing external perturbations to said previously trainedartificial neural network to vary the particular input patterns aspresented at the input portion of said previously trained artificialneural network to thereby establish, upon the production of an output inresponse to the particular input pattern as presented, an input-outputdata pairing relationship.
 20. The method of claim 21 wherein thepredefined criteria have been selected to represent a particular desiredpattern within a data output from said previously trained neuralnetwork, whereby, upon satisfaction of said predefined criteria, thedetermined input-output pairing relationship identifies a data inputthat results in the data output that satisfies such predefined criteria.21. An artificial neural network-based system for obtaining, for aspecified knowledge domain in a given field of endeavor as representedin a neural network, a desired concept within such predefined field ofendeavor, comprising a neural network portion including an artificialneural network object having a plurality of interrelated nodes disposedin at least an input layer, an output layer, and at least oneintermediate layer, said nodes of said input layer connected to receivedata inputs supplied thereto, at least one node of said neural networkobject connected to receive a selection criteria input, said neuralnetwork object having been previously trained in a given predefinedfield of endeavor, with non-restrictive selection criteria input, toestablish a particular knowledge domain in said neural network object,said neural network object, after such training, operable in accordancewith the constraints embodied in its design and the establishedknowledge domain, when a non-restrictive selection criteria input isbeing supplied thereto, to produce standard outputs therefrom which arerepresentative of concepts consistent with the training of said neuralnetwork object, a neural network perturbation portion for perturbingsaid artificial neural network object to drive an operation of saidneural network object and to effect changes, subject to constraintsembodied in the design of the artificial neural network that remainunperturbed and when a non-restrictive selection criteria input issupplied, in the outputs produced by said artificial neural networkobject, said neural network object being normally operable, upon beingperturbed by said neural network perturbation, when a predefinedrestrictive selection criteria as preselected by a user is supplied as aselection criteria input and such selection criteria is satisfied, toproduce an output therefrom, which output is representative of a desiredconcept, and a system output portion for receiving outputs that arerepresentative of a desired concept in accordance with the predefinedrestrictive selection criteria.
 22. The system of claim 21 wherein saidneural network object includes first and second artificial neuralnetworks, said first artificial neural network connected to receive saiddata inputs and to produce data outputs in response thereto, said secondartificial neural network connected to receive said predefined selectioncriteria and at least some of said data outputs produced by said firstartificial neural network.
 23. An artificial neural network-based systemfor determining, for a specified knowledge domain in a given field ofendeavor as represented in a neural network, desired concepts andrelationships within such predefined field of endeavor, comprising aneural network portion including an artificial neural network that hasan input portion and an output portion and is operable to produceoutputs at said output portion when inputs are supplied to saidartificial neural network at the input portion thereof, said artificialneural network having been previously trained in accordance withtraining exemplars in a given predefined field of endeavor to establisha particular knowledge domain therein and being normally operable inaccordance with the constraints embodied in its design and theestablished knowledge domain to produce standard outputs in response toa pattern of inputs supplied to said previously trained artificialneural network at the input portion thereof, a neural networkperturbation portion for perturbing said neural network portion todegrade said neural network portion and the knowledge domain of saidneural network to effect changes, subject to constraints embodied in thedesign of the artificial neural network that remain unperturbed, in theoutputs produced by said neural network portion at the output portion ofsaid neural network portion, said changed outputs differing from thestandard outputs that would be produced by said neural network portionif the knowledge domain of said neural network were not degraded, eachoutput so produced establishing an input-perturbation-output mappingrelationship within said predefined field of endeavor, and a monitorportion associated with said neural network portion to observe theoutputs produced at the output portion of the artificial neural networkover a period of time and to identify from among said outputs soproduced particular outputs that satisfy predefined criteria aspreselected by a user, identification of an output that satisfies thepredefined criteria determining a desired concept within the predefinedfield of endeavor, which desired concept is associated with a particularinput-perturbation-output mapping relationship established duringoperation of said system.